166 lines
5.5 KiB
Python
166 lines
5.5 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class IEBlock(nn.Module):
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def __init__(self, input_dim, hid_dim, output_dim, num_node):
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super(IEBlock, self).__init__()
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self.input_dim = input_dim
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self.hid_dim = hid_dim
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self.output_dim = output_dim
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self.num_node = num_node
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self._build()
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def _build(self):
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self.spatial_proj = nn.Sequential(
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nn.Linear(self.input_dim, self.hid_dim),
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nn.LeakyReLU(),
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nn.Linear(self.hid_dim, self.hid_dim // 4)
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)
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self.channel_proj = nn.Linear(self.num_node, self.num_node)
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torch.nn.init.eye_(self.channel_proj.weight)
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self.output_proj = nn.Linear(self.hid_dim // 4, self.output_dim)
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def forward(self, x):
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x = self.spatial_proj(x.permute(0, 2, 1))
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x = x.permute(0, 2, 1) + self.channel_proj(x.permute(0, 2, 1))
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x = self.output_proj(x.permute(0, 2, 1))
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x = x.permute(0, 2, 1)
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return x
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class Model(nn.Module):
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"""
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Paper link: https://arxiv.org/abs/2207.01186
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"""
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def __init__(self, configs, chunk_size=24):
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"""
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chunk_size: int, reshape T into [num_chunks, chunk_size]
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"""
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super(Model, self).__init__()
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self.task_name = configs.task_name
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self.seq_len = configs.seq_len
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if self.task_name == 'classification' or self.task_name == 'anomaly_detection' or self.task_name == 'imputation':
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self.pred_len = configs.seq_len
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else:
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self.pred_len = configs.pred_len
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if configs.task_name == 'long_term_forecast' or configs.task_name == 'short_term_forecast':
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self.chunk_size = min(configs.pred_len, configs.seq_len, chunk_size)
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else:
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self.chunk_size = min(configs.seq_len, chunk_size)
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# assert (self.seq_len % self.chunk_size == 0)
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if self.seq_len % self.chunk_size != 0:
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self.seq_len += (self.chunk_size - self.seq_len % self.chunk_size) # padding in order to ensure complete division
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self.num_chunks = self.seq_len // self.chunk_size
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self.d_model = configs.d_model
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self.enc_in = configs.enc_in
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self.dropout = configs.dropout
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if self.task_name == 'classification':
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self.act = F.gelu
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self.dropout = nn.Dropout(configs.dropout)
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self.projection = nn.Linear(configs.enc_in * configs.seq_len, configs.num_class)
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self._build()
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def _build(self):
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self.layer_1 = IEBlock(
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input_dim=self.chunk_size,
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hid_dim=self.d_model // 4,
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output_dim=self.d_model // 4,
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num_node=self.num_chunks
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)
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self.chunk_proj_1 = nn.Linear(self.num_chunks, 1)
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self.layer_2 = IEBlock(
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input_dim=self.chunk_size,
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hid_dim=self.d_model // 4,
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output_dim=self.d_model // 4,
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num_node=self.num_chunks
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)
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self.chunk_proj_2 = nn.Linear(self.num_chunks, 1)
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self.layer_3 = IEBlock(
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input_dim=self.d_model // 2,
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hid_dim=self.d_model // 2,
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output_dim=self.pred_len,
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num_node=self.enc_in
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)
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self.ar = nn.Linear(self.seq_len, self.pred_len)
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def encoder(self, x):
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B, T, N = x.size()
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# padding
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x = torch.cat([x, torch.zeros((B, self.seq_len - T, N)).to(x.device)], dim=1)
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highway = self.ar(x.permute(0, 2, 1))
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highway = highway.permute(0, 2, 1)
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# continuous sampling
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x1 = x.reshape(B, self.num_chunks, self.chunk_size, N)
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x1 = x1.permute(0, 3, 2, 1)
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x1 = x1.reshape(-1, self.chunk_size, self.num_chunks)
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x1 = self.layer_1(x1)
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x1 = self.chunk_proj_1(x1).squeeze(dim=-1)
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# interval sampling
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x2 = x.reshape(B, self.chunk_size, self.num_chunks, N)
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x2 = x2.permute(0, 3, 1, 2)
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x2 = x2.reshape(-1, self.chunk_size, self.num_chunks)
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x2 = self.layer_2(x2)
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x2 = self.chunk_proj_2(x2).squeeze(dim=-1)
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x3 = torch.cat([x1, x2], dim=-1)
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x3 = x3.reshape(B, N, -1)
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x3 = x3.permute(0, 2, 1)
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out = self.layer_3(x3)
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out = out + highway
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return out
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def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
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return self.encoder(x_enc)
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def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask):
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return self.encoder(x_enc)
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def anomaly_detection(self, x_enc):
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return self.encoder(x_enc)
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def classification(self, x_enc, x_mark_enc):
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enc_out = self.encoder(x_enc)
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# Output
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output = enc_out.reshape(enc_out.shape[0], -1) # (batch_size, seq_length * d_model)
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output = self.projection(output) # (batch_size, num_classes)
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return output
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def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
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if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
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dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)
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return dec_out[:, -self.pred_len:, :] # [B, L, D]
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if self.task_name == 'imputation':
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dec_out = self.imputation(x_enc, x_mark_enc, x_dec, x_mark_dec, mask)
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return dec_out # [B, L, D]
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if self.task_name == 'anomaly_detection':
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dec_out = self.anomaly_detection(x_enc)
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return dec_out # [B, L, D]
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if self.task_name == 'classification':
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dec_out = self.classification(x_enc, x_mark_enc)
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return dec_out # [B, N]
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return None
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